{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用逻辑回归完成员工离职预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该数据集来源于Kaggle竞赛平台，共计14999条样本和10个特征，本案例希望通过分析现有的员工离职数据，建立模型预测有可能离职的员工。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4>**内容概要**</font><br>\n",
    "\n",
    "1 数据概览<br>\n",
    "2 数据预处理<br>\n",
    "3 数据探索及可视化<br>\n",
    "&emsp;3.1 标签探索：员工离职状况概览<br>\n",
    "&emsp;3.2 特征探索：员工对公司满意度与是否离职的关系<br>\n",
    "&emsp;3.3 特征探索：最新考核评估与是否离职的关系<br>\n",
    "&emsp;3.4 特征探索：参加项目数与是否离职的关系<br>\n",
    "&emsp;3.5 特征探索：平均每月工作时长与是否离职的关系<br>\n",
    "&emsp;3.6 特征探索：工作年限与是否离职的关系<br>\n",
    "&emsp;3.7 特征探索：是否发生工作事故与是否离职的关系<br>\n",
    "&emsp;3.8 特征探索：五年内是否晋升与是否离职的关系<br>\n",
    "&emsp;3.9 特征探索：岗位与是否离职的关系<br>\n",
    "&emsp;3.10 特征探索：薪资水平与是否离职的关系<br>\n",
    "4 特征工程&建立模型<br>\n",
    "&emsp;4.1 编码：将文本型变量变为数值型<br>\n",
    "&emsp;4.2 提取特征和标签并切分数据集<br>\n",
    "&emsp;4.3 初步建模：建立benchmark<br>\n",
    "&emsp;4.4 测试数据归一化对模型结果的影响<br>\n",
    "5 模型调优<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 数据概览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:12:40.598702800Z",
     "start_time": "2024-05-28T01:12:40.584747500Z"
    }
   },
   "outputs": [],
   "source": [
    "# 导入相应模块和包\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['Simhei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:12:59.182293800Z",
     "start_time": "2024-05-28T01:12:59.097599400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n0                0.38             0.53               2                   157   \n1                0.80             0.86               5                   262   \n2                0.11             0.88               7                   272   \n3                0.72             0.87               5                   223   \n4                0.37             0.52               2                   159   \n\n   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n0                   3              0     1                      0  sales   \n1                   6              0     1                      0  sales   \n2                   4              0     1                      0  sales   \n3                   5              0     1                      0  sales   \n4                   3              0     1                      0  sales   \n\n   salary  \n0     low  \n1  medium  \n2  medium  \n3     low  \n4     low  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>satisfaction_level</th>\n      <th>last_evaluation</th>\n      <th>number_project</th>\n      <th>average_montly_hours</th>\n      <th>time_spend_company</th>\n      <th>Work_accident</th>\n      <th>left</th>\n      <th>promotion_last_5years</th>\n      <th>sales</th>\n      <th>salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.38</td>\n      <td>0.53</td>\n      <td>2</td>\n      <td>157</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>sales</td>\n      <td>low</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.80</td>\n      <td>0.86</td>\n      <td>5</td>\n      <td>262</td>\n      <td>6</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>sales</td>\n      <td>medium</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.11</td>\n      <td>0.88</td>\n      <td>7</td>\n      <td>272</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>sales</td>\n      <td>medium</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.72</td>\n      <td>0.87</td>\n      <td>5</td>\n      <td>223</td>\n      <td>5</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>sales</td>\n      <td>low</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.37</td>\n      <td>0.52</td>\n      <td>2</td>\n      <td>159</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>sales</td>\n      <td>low</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入原始数据\n",
    "data = pd.read_csv(\"data/HR_comma_sep.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:13:06.098551800Z",
     "start_time": "2024-05-28T01:13:06.013663600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(14999, 10)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:13:07.927368900Z",
     "start_time": "2024-05-28T01:13:07.862482100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   satisfaction_level     14999 non-null  float64\n",
      " 1   last_evaluation        14999 non-null  float64\n",
      " 2   number_project         14999 non-null  int64  \n",
      " 3   average_montly_hours   14999 non-null  int64  \n",
      " 4   time_spend_company     14999 non-null  int64  \n",
      " 5   Work_accident          14999 non-null  int64  \n",
      " 6   left                   14999 non-null  int64  \n",
      " 7   promotion_last_5years  14999 non-null  int64  \n",
      " 8   sales                  14999 non-null  object \n",
      " 9   salary                 14999 non-null  object \n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:13:21.232674700Z",
     "start_time": "2024-05-28T01:13:21.141636300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                         count        mean        std    min     25%     50%  \\\nsatisfaction_level     14999.0    0.612834   0.248631   0.09    0.44    0.64   \nlast_evaluation        14999.0    0.716102   0.171169   0.36    0.56    0.72   \nnumber_project         14999.0    3.803054   1.232592   2.00    3.00    4.00   \naverage_montly_hours   14999.0  201.050337  49.943099  96.00  156.00  200.00   \ntime_spend_company     14999.0    3.498233   1.460136   2.00    3.00    3.00   \nWork_accident          14999.0    0.144610   0.351719   0.00    0.00    0.00   \nleft                   14999.0    0.238083   0.425924   0.00    0.00    0.00   \npromotion_last_5years  14999.0    0.021268   0.144281   0.00    0.00    0.00   \n\n                          75%    max  \nsatisfaction_level       0.82    1.0  \nlast_evaluation          0.87    1.0  \nnumber_project           5.00    7.0  \naverage_montly_hours   245.00  310.0  \ntime_spend_company       4.00   10.0  \nWork_accident            0.00    1.0  \nleft                     0.00    1.0  \npromotion_last_5years    0.00    1.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>satisfaction_level</th>\n      <td>14999.0</td>\n      <td>0.612834</td>\n      <td>0.248631</td>\n      <td>0.09</td>\n      <td>0.44</td>\n      <td>0.64</td>\n      <td>0.82</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>last_evaluation</th>\n      <td>14999.0</td>\n      <td>0.716102</td>\n      <td>0.171169</td>\n      <td>0.36</td>\n      <td>0.56</td>\n      <td>0.72</td>\n      <td>0.87</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>number_project</th>\n      <td>14999.0</td>\n      <td>3.803054</td>\n      <td>1.232592</td>\n      <td>2.00</td>\n      <td>3.00</td>\n      <td>4.00</td>\n      <td>5.00</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>average_montly_hours</th>\n      <td>14999.0</td>\n      <td>201.050337</td>\n      <td>49.943099</td>\n      <td>96.00</td>\n      <td>156.00</td>\n      <td>200.00</td>\n      <td>245.00</td>\n      <td>310.0</td>\n    </tr>\n    <tr>\n      <th>time_spend_company</th>\n      <td>14999.0</td>\n      <td>3.498233</td>\n      <td>1.460136</td>\n      <td>2.00</td>\n      <td>3.00</td>\n      <td>3.00</td>\n      <td>4.00</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>Work_accident</th>\n      <td>14999.0</td>\n      <td>0.144610</td>\n      <td>0.351719</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>left</th>\n      <td>14999.0</td>\n      <td>0.238083</td>\n      <td>0.425924</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>promotion_last_5years</th>\n      <td>14999.0</td>\n      <td>0.021268</td>\n      <td>0.144281</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 简单描述统计(数值型特征)\n",
    "data.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从简单描述统计结果中可以看出：<br>\n",
    "- 员工对公司满意度：范围[0.09,1]，中位数为0.64，平均值为0.61\n",
    "- 最新考核评估：范围[0.36,1]，中位数为0.72，平均值为0.716\n",
    "- 项目数：范围[2,7]，中位数为4，平均值为3.8\n",
    "- 平均每月工作时长：范围[96,310]，中位数为200，平均值为201\n",
    "- 工作年限：范围[2,10]，中位数为3，平均值为3.5\n",
    "- 工作中出现工作事故的占比为14.5%\n",
    "- 已经离职的占比为23.8%\n",
    "- 在5年内晋升的占比为2.1%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:13:27.232137500Z",
     "start_time": "2024-05-28T01:13:27.183591500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "        count unique    top  freq\nsales   14999     10  sales  4140\nsalary  14999      3    low  7316",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>unique</th>\n      <th>top</th>\n      <th>freq</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>sales</th>\n      <td>14999</td>\n      <td>10</td>\n      <td>sales</td>\n      <td>4140</td>\n    </tr>\n    <tr>\n      <th>salary</th>\n      <td>14999</td>\n      <td>3</td>\n      <td>low</td>\n      <td>7316</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 简单描述统计（文本型特征）\n",
    "data.describe(include=['O']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 员工岗位一共有10种，其中人数最多的岗位是销售，一共4140人\n",
    "- 员工薪资水平一共有3个等级，其中最多的是低等水平，一共7316人"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:15:17.869589200Z",
     "start_time": "2024-05-28T01:15:17.809650900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "satisfaction_level       0\nlast_evaluation          0\nnumber_project           0\naverage_montly_hours     0\ntime_spend_company       0\nWork_accident            0\nleft                     0\npromotion_last_5years    0\nsales                    0\nsalary                   0\ndtype: int64"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:14:33.629326800Z",
     "start_time": "2024-05-28T01:14:33.565971100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0        False\n1        False\n2        False\n3        False\n4        False\n         ...  \n14994     True\n14995     True\n14996     True\n14997     True\n14998     True\nLength: 14999, dtype: bool"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated()\n",
    "# data.iloc[-2:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该数据并没有缺失值，因此不用处理缺失值。下面在看一下重复值的状况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:15:22.189299500Z",
     "start_time": "2024-05-28T01:15:22.161133300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "3008"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看重复值\n",
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:15:23.915967600Z",
     "start_time": "2024-05-28T01:15:23.853722500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "3     0.429562\n2     0.216281\n4     0.170478\n5     0.098207\n6     0.047870\n10    0.014268\n7     0.012534\n8     0.010801\nName: time_spend_company, dtype: float64"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data.columns\n",
    "data.time_spend_company.value_counts(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里可以看到该数据集有3008条重复值，但是这个数据集中并没有员工ID这样的唯一标识字段，所以这里选择不处理重复值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:15:45.822831800Z",
     "start_time": "2024-05-28T01:15:45.407907Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x500 with 5 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制箱型图，查看异常值状况\n",
    "plt.figure(figsize=(20,5),dpi=100)\n",
    "for i in range(5):\n",
    "    plt.subplot(1,5,i+1)\n",
    "    plt.boxplot(data.iloc[:,i])\n",
    "    plt.xlabel(data.columns[i])\n",
    "    plt.xticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从箱型图结果中可以看出，除了工作年限之外，其他特征均无异常值。<br>该异常值也反映了该公司基本上以年轻人为主，大部分是工作年限为4年以内。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 数据探索及可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 标签探索：员工离职状况概览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:30:15.349243200Z",
     "start_time": "2024-05-28T01:30:15.302675100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0    0.761917\n1    0.238083\nName: left, dtype: float64"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"left\"].value_counts(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:16:00.004096Z",
     "start_time": "2024-05-28T01:15:59.959019800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0    0.761917\n1    0.238083\nName: left, dtype: float64"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"left\"].value_counts()/data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "?plt.pie"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-28T01:33:47.441298Z",
     "start_time": "2024-05-28T01:33:47.340974900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:16:08.737094Z",
     "start_time": "2024-05-28T01:16:08.618497500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 600x600 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v1 = data[\"left\"].value_counts()\n",
    "\n",
    "# 绘制饼图：在职和离职员工占比\n",
    "plt.figure(figsize=(6,6))      \n",
    "plt.pie(v1                             #饼图原始数据\n",
    "        ,radius=1                      #饼图半径\n",
    "        ,autopct=\"%.2f%%\"              #自动添加百分比显示，可以采用格式化的方法显示\n",
    "        ,labels=[\"在职\",\"离职\"]         #为饼图添加标签说明，类似于图例说明\n",
    "        ,labeldistance=1.1             #设置各扇形标签（图例）与圆心的距离\n",
    "        ,pctdistance=0.8               #设置百分比标签与圆心的距离\n",
    "        ,textprops={\"fontsize\":15}     #设置饼图中文本的属性，如字体大小、颜色等\n",
    "        ,wedgeprops={\"width\":0.4}      #设置环的宽度\n",
    "       );    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 特征探索：员工对公司满意度与是否离职的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:19:57.442399300Z",
     "start_time": "2024-05-28T01:19:56.889879300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x800 with 3 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制直方图和箱型图\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "grid = plt.GridSpec(2, 2, wspace=0.2, hspace=0.3) # wspace表示左右子图间距，hspace表示上下子图间距\n",
    "\n",
    "plt.subplot(grid[0, :])\n",
    "sns.histplot(data=data, x=\"satisfaction_level\", hue=\"left\",kde=True) # kde表示是否绘制核密度图,核密度图是直方图的平滑版本\n",
    "plt.subplot(grid[1, 0])\n",
    "sns.boxplot(data=data,y=\"satisfaction_level\",color=\"#ff7675\",width=0.3)\n",
    "plt.subplot(grid[1, 1])\n",
    "sns.boxplot(data=data,y=\"satisfaction_level\",x=\"left\",width=0.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从直方图分布来看，离职员工普遍对公司满意度不高（且很大一部分离职员工的满意度在最下限），而在职员工普遍对公司比较满意，满意度普遍在0.5以上，这个也比较符合我们的常识：如果一个员工对公司满意度非常低，那么他离离职也不远了。<br>\n",
    "从箱型图上也可以看出，在职员工对公司满意度的中位数明显比离职员工多了一大截，另外离职人员中没有满意度为1的评价。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **对公司满意度极低的离职员工有哪些共性？**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T01:22:01.473053800Z",
     "start_time": "2024-05-28T01:22:01.449623500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.09"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"satisfaction_level\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:15:16.514487900Z",
     "start_time": "2024-05-28T02:15:16.466667Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                       count        mean           std     min     25%  \\\nsatisfaction_level     195.0    0.090000  9.739457e-17    0.09    0.09   \nlast_evaluation        195.0    0.871538  7.078858e-02    0.62    0.82   \nnumber_project         195.0    6.179487  5.951607e-01    3.00    6.00   \naverage_montly_hours   195.0  275.692308  2.031067e+01  214.00  257.50   \ntime_spend_company     195.0    4.107692  3.980524e-01    2.00    4.00   \nWork_accident          195.0    0.020513  1.421113e-01    0.00    0.00   \nleft                   195.0    1.000000  0.000000e+00    1.00    1.00   \npromotion_last_5years  195.0    0.000000  0.000000e+00    0.00    0.00   \n\n                          50%      75%     max  \nsatisfaction_level       0.09    0.090    0.09  \nlast_evaluation          0.87    0.935    0.98  \nnumber_project           6.00    6.500    7.00  \naverage_montly_hours   275.00  294.000  310.00  \ntime_spend_company       4.00    4.000    5.00  \nWork_accident            0.00    0.000    1.00  \nleft                     1.00    1.000    1.00  \npromotion_last_5years    0.00    0.000    0.00  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>satisfaction_level</th>\n      <td>195.0</td>\n      <td>0.090000</td>\n      <td>9.739457e-17</td>\n      <td>0.09</td>\n      <td>0.09</td>\n      <td>0.09</td>\n      <td>0.090</td>\n      <td>0.09</td>\n    </tr>\n    <tr>\n      <th>last_evaluation</th>\n      <td>195.0</td>\n      <td>0.871538</td>\n      <td>7.078858e-02</td>\n      <td>0.62</td>\n      <td>0.82</td>\n      <td>0.87</td>\n      <td>0.935</td>\n      <td>0.98</td>\n    </tr>\n    <tr>\n      <th>number_project</th>\n      <td>195.0</td>\n      <td>6.179487</td>\n      <td>5.951607e-01</td>\n      <td>3.00</td>\n      <td>6.00</td>\n      <td>6.00</td>\n      <td>6.500</td>\n      <td>7.00</td>\n    </tr>\n    <tr>\n      <th>average_montly_hours</th>\n      <td>195.0</td>\n      <td>275.692308</td>\n      <td>2.031067e+01</td>\n      <td>214.00</td>\n      <td>257.50</td>\n      <td>275.00</td>\n      <td>294.000</td>\n      <td>310.00</td>\n    </tr>\n    <tr>\n      <th>time_spend_company</th>\n      <td>195.0</td>\n      <td>4.107692</td>\n      <td>3.980524e-01</td>\n      <td>2.00</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>4.000</td>\n      <td>5.00</td>\n    </tr>\n    <tr>\n      <th>Work_accident</th>\n      <td>195.0</td>\n      <td>0.020513</td>\n      <td>1.421113e-01</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.000</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>left</th>\n      <td>195.0</td>\n      <td>1.000000</td>\n      <td>0.000000e+00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.000</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>promotion_last_5years</th>\n      <td>195.0</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.000</td>\n      <td>0.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data[\"satisfaction_level\"]==0.09].describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，对公司满意度极低的离职员工，普遍负责的项目数量比较多（6个左右），比全部员工的平均项目数多2个；这部分员工的每月工作时长基本上在275分钟，这意味着每月22个工作日平均每个工作日他们工作了12.5个小时，存在着严重的加班现象；并且他们在工作任务大、加班严重的情况下，最近5年内还没有晋升。现在，我们应该非常清楚这部分人对公司的满意度极低并且离职的原因了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **对公司满意度极低但是仍然在职的员工有哪些共性？**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的直方图中，我们发现有一部分在职员工对公司满意度也非常低，接下来我们一起探索一下，这部分员工有什么共性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:16:17.237528600Z",
     "start_time": "2024-05-28T02:16:17.187134200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                       count        mean           std     min       25%  \\\nsatisfaction_level      26.0    0.120000  8.491573e-17    0.12    0.1200   \nlast_evaluation         26.0    0.670385  1.644258e-01    0.39    0.5575   \nnumber_project          26.0    4.423077  1.205755e+00    2.00    3.2500   \naverage_montly_hours    26.0  225.576923  4.575340e+01  110.00  191.7500   \ntime_spend_company      26.0    4.269231  1.282426e+00    2.00    3.0000   \nWork_accident           26.0    0.076923  2.717465e-01    0.00    0.0000   \nleft                    26.0    0.000000  0.000000e+00    0.00    0.0000   \npromotion_last_5years   26.0    0.000000  0.000000e+00    0.00    0.0000   \n\n                          50%       75%     max  \nsatisfaction_level       0.12    0.1200    0.12  \nlast_evaluation          0.63    0.8175    0.95  \nnumber_project           4.50    5.0000    6.00  \naverage_montly_hours   238.50  257.7500  287.00  \ntime_spend_company       4.00    5.0000    6.00  \nWork_accident            0.00    0.0000    1.00  \nleft                     0.00    0.0000    0.00  \npromotion_last_5years    0.00    0.0000    0.00  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>satisfaction_level</th>\n      <td>26.0</td>\n      <td>0.120000</td>\n      <td>8.491573e-17</td>\n      <td>0.12</td>\n      <td>0.1200</td>\n      <td>0.12</td>\n      <td>0.1200</td>\n      <td>0.12</td>\n    </tr>\n    <tr>\n      <th>last_evaluation</th>\n      <td>26.0</td>\n      <td>0.670385</td>\n      <td>1.644258e-01</td>\n      <td>0.39</td>\n      <td>0.5575</td>\n      <td>0.63</td>\n      <td>0.8175</td>\n      <td>0.95</td>\n    </tr>\n    <tr>\n      <th>number_project</th>\n      <td>26.0</td>\n      <td>4.423077</td>\n      <td>1.205755e+00</td>\n      <td>2.00</td>\n      <td>3.2500</td>\n      <td>4.50</td>\n      <td>5.0000</td>\n      <td>6.00</td>\n    </tr>\n    <tr>\n      <th>average_montly_hours</th>\n      <td>26.0</td>\n      <td>225.576923</td>\n      <td>4.575340e+01</td>\n      <td>110.00</td>\n      <td>191.7500</td>\n      <td>238.50</td>\n      <td>257.7500</td>\n      <td>287.00</td>\n    </tr>\n    <tr>\n      <th>time_spend_company</th>\n      <td>26.0</td>\n      <td>4.269231</td>\n      <td>1.282426e+00</td>\n      <td>2.00</td>\n      <td>3.0000</td>\n      <td>4.00</td>\n      <td>5.0000</td>\n      <td>6.00</td>\n    </tr>\n    <tr>\n      <th>Work_accident</th>\n      <td>26.0</td>\n      <td>0.076923</td>\n      <td>2.717465e-01</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>left</th>\n      <td>26.0</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>promotion_last_5years</th>\n      <td>26.0</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ls = data.loc[(data[\"satisfaction_level\"]<0.13)&(data[\"left\"]==0),:]\n",
    "Ls.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:18:37.151653200Z",
     "start_time": "2024-05-28T02:18:37.027700400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ls[\"average_montly_hours\"].plot(kind=\"box\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从对这部分员工的描述统计中可以看出，这部分员工也是5年内没有晋升，并且还有一半以上的人存在着加班严重的状况。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 特征探索：最新考核评估与是否离职的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:18:50.930323100Z",
     "start_time": "2024-05-28T02:18:50.462123500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x800 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.subplot(211)   # 211表示两行一列，第一块\n",
    "sns.histplot(data=data, x=\"last_evaluation\", hue=\"left\",kde=True)\n",
    "plt.subplot(212)\n",
    "sns.boxplot(data=data,y=\"last_evaluation\",x=\"left\",width=0.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，离职人员的最新考核评估相对来说比较高，但是存在着两极分化的现象，即一部分人考核相对偏低，另一部分人考核相对比较高，而处在中间状态的人比较少。根据这些信息，我们可以猜测，离职的这部分员工中，一部分人很优秀，但是被严重的加班状况给劝退了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 特征探索：参加项目数与是否离职的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:21:15.448831400Z",
     "start_time": "2024-05-28T02:21:15.398304500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "2    2388\n5    2761\n7     256\n6    1174\n4    4365\n3    4055\nName: number_project, dtype: int64"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v2 = data[\"number_project\"].value_counts(sort=False)\n",
    "v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:29:46.881003100Z",
     "start_time": "2024-05-28T02:29:46.795857800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "left                   0         1\nnumber_project                    \n2               0.343802  0.656198\n3               0.982244  0.017756\n4               0.906300  0.093700\n5               0.778341  0.221659\n6               0.442078  0.557922\n7               0.000000  1.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>left</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>number_project</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>0.343802</td>\n      <td>0.656198</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.982244</td>\n      <td>0.017756</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.906300</td>\n      <td>0.093700</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.778341</td>\n      <td>0.221659</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.442078</td>\n      <td>0.557922</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v2_rate = data.groupby([\"number_project\",\"left\"],as_index=False).size()  # size表示参与人数\n",
    "v2_rate = v2_rate.pivot(index=\"number_project\",columns=\"left\",values=\"size\")\n",
    "v2_rate = v2_rate.div(v2_rate.sum(axis=1), axis=0)\n",
    "v2_rate.fillna(0,inplace=True)\n",
    "v2_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:24:29.136955600Z",
     "start_time": "2024-05-28T02:24:29.112959400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "['magma',\n 'inferno',\n 'plasma',\n 'viridis',\n 'cividis',\n 'twilight',\n 'twilight_shifted',\n 'turbo',\n 'Blues',\n 'BrBG',\n 'BuGn',\n 'BuPu',\n 'CMRmap',\n 'GnBu',\n 'Greens',\n 'Greys',\n 'OrRd',\n 'Oranges',\n 'PRGn',\n 'PiYG',\n 'PuBu',\n 'PuBuGn',\n 'PuOr',\n 'PuRd',\n 'Purples',\n 'RdBu',\n 'RdGy',\n 'RdPu',\n 'RdYlBu',\n 'RdYlGn',\n 'Reds',\n 'Spectral',\n 'Wistia',\n 'YlGn',\n 'YlGnBu',\n 'YlOrBr',\n 'YlOrRd',\n 'afmhot',\n 'autumn',\n 'binary',\n 'bone',\n 'brg',\n 'bwr',\n 'cool',\n 'coolwarm',\n 'copper',\n 'cubehelix',\n 'flag',\n 'gist_earth',\n 'gist_gray',\n 'gist_heat',\n 'gist_ncar',\n 'gist_rainbow',\n 'gist_stern',\n 'gist_yarg',\n 'gnuplot',\n 'gnuplot2',\n 'gray',\n 'hot',\n 'hsv',\n 'jet',\n 'nipy_spectral',\n 'ocean',\n 'pink',\n 'prism',\n 'rainbow',\n 'seismic',\n 'spring',\n 'summer',\n 'terrain',\n 'winter',\n 'Accent',\n 'Dark2',\n 'Paired',\n 'Pastel1',\n 'Pastel2',\n 'Set1',\n 'Set2',\n 'Set3',\n 'tab10',\n 'tab20',\n 'tab20b',\n 'tab20c',\n 'grey',\n 'gist_grey',\n 'gist_yerg',\n 'Grays',\n 'magma_r',\n 'inferno_r',\n 'plasma_r',\n 'viridis_r',\n 'cividis_r',\n 'twilight_r',\n 'twilight_shifted_r',\n 'turbo_r',\n 'Blues_r',\n 'BrBG_r',\n 'BuGn_r',\n 'BuPu_r',\n 'CMRmap_r',\n 'GnBu_r',\n 'Greens_r',\n 'Greys_r',\n 'OrRd_r',\n 'Oranges_r',\n 'PRGn_r',\n 'PiYG_r',\n 'PuBu_r',\n 'PuBuGn_r',\n 'PuOr_r',\n 'PuRd_r',\n 'Purples_r',\n 'RdBu_r',\n 'RdGy_r',\n 'RdPu_r',\n 'RdYlBu_r',\n 'RdYlGn_r',\n 'Reds_r',\n 'Spectral_r',\n 'Wistia_r',\n 'YlGn_r',\n 'YlGnBu_r',\n 'YlOrBr_r',\n 'YlOrRd_r',\n 'afmhot_r',\n 'autumn_r',\n 'binary_r',\n 'bone_r',\n 'brg_r',\n 'bwr_r',\n 'cool_r',\n 'coolwarm_r',\n 'copper_r',\n 'cubehelix_r',\n 'flag_r',\n 'gist_earth_r',\n 'gist_gray_r',\n 'gist_heat_r',\n 'gist_ncar_r',\n 'gist_rainbow_r',\n 'gist_stern_r',\n 'gist_yarg_r',\n 'gnuplot_r',\n 'gnuplot2_r',\n 'gray_r',\n 'hot_r',\n 'hsv_r',\n 'jet_r',\n 'nipy_spectral_r',\n 'ocean_r',\n 'pink_r',\n 'prism_r',\n 'rainbow_r',\n 'seismic_r',\n 'spring_r',\n 'summer_r',\n 'terrain_r',\n 'winter_r',\n 'Accent_r',\n 'Dark2_r',\n 'Paired_r',\n 'Pastel1_r',\n 'Pastel2_r',\n 'Set1_r',\n 'Set2_r',\n 'Set3_r',\n 'tab10_r',\n 'tab20_r',\n 'tab20b_r',\n 'tab20c_r',\n 'rocket',\n 'rocket_r',\n 'mako',\n 'mako_r',\n 'icefire',\n 'icefire_r',\n 'vlag',\n 'vlag_r',\n 'flare',\n 'flare_r',\n 'crest',\n 'crest_r']"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.colormaps()   # 显示所有颜色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:29:53.826858800Z",
     "start_time": "2024-05-28T02:29:53.426447300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x400 with 3 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = plt.get_cmap('Set3')(np.linspace(0.2, 0.7, len(v2)))\n",
    "plt.figure(figsize=(12,4),dpi=100)\n",
    "\n",
    "# 绘制参与不同项目数的人员离职数量\n",
    "plt.subplot(131)\n",
    "sns.histplot(data=data,y=\"number_project\",hue=\"left\"\n",
    "             ,multiple='stack'\n",
    "             ,shrink=0.8\n",
    "             ,discrete=True\n",
    "             ,alpha=0.7)\n",
    "\n",
    "# 绘制所参加项目与是否离职的关系\n",
    "plt.subplot(132)\n",
    "plt.barh(y=v2_rate.index,width=v2_rate[1],color=\"#ff7f0e\",alpha=0.7)\n",
    "plt.barh(y=v2_rate.index,width=v2_rate[0],left=v2_rate[1],color=\"#1f77b4\",alpha=0.7)\n",
    "\n",
    "# 绘制各项目数所占百分比环形图\n",
    "plt.subplot(133)\n",
    "plt.pie(v2,autopct=\"%.2f%%\",radius=1,pctdistance=0.8,labels=v2.index,colors=colors,wedgeprops={\"width\":0.4});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述图表中可以看出，离职人员所占比例随着项目数的增多而增大（项目数为2的离职比例是个特例），此外，离职人员所占比例较高的项目数2、6、7在项目总数上占比相对较少，可以推测：\n",
    "- 项目数为2的这部分人有可能是工作能力不被认可，其离职人数也相对较多；\n",
    "- 项目数为6和7的这部分员工，可能工作能力较强，跳槽相对比较容易，自然离职比例也相对比较高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:00.426159200Z",
     "start_time": "2024-05-28T02:30:00.334832900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                        count        mean        std    min     25%     50%  \\\nsatisfaction_level     2388.0    0.478769   0.158772   0.10    0.39    0.43   \nlast_evaluation        2388.0    0.568505   0.134159   0.36    0.48    0.53   \nnumber_project         2388.0    2.000000   0.000000   2.00    2.00    2.00   \naverage_montly_hours   2388.0  160.342546  38.849089  96.00  136.00  149.00   \ntime_spend_company     2388.0    3.180486   0.946232   2.00    3.00    3.00   \nWork_accident          2388.0    0.092965   0.290444   0.00    0.00    0.00   \nleft                   2388.0    0.656198   0.475076   0.00    0.00    1.00   \npromotion_last_5years  2388.0    0.015494   0.123533   0.00    0.00    0.00   \n\n                          75%    max  \nsatisfaction_level       0.49    1.0  \nlast_evaluation          0.57    1.0  \nnumber_project           2.00    2.0  \naverage_montly_hours   160.00  310.0  \ntime_spend_company       3.00   10.0  \nWork_accident            0.00    1.0  \nleft                     1.00    1.0  \npromotion_last_5years    0.00    1.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>satisfaction_level</th>\n      <td>2388.0</td>\n      <td>0.478769</td>\n      <td>0.158772</td>\n      <td>0.10</td>\n      <td>0.39</td>\n      <td>0.43</td>\n      <td>0.49</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>last_evaluation</th>\n      <td>2388.0</td>\n      <td>0.568505</td>\n      <td>0.134159</td>\n      <td>0.36</td>\n      <td>0.48</td>\n      <td>0.53</td>\n      <td>0.57</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>number_project</th>\n      <td>2388.0</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.00</td>\n      <td>2.00</td>\n      <td>2.00</td>\n      <td>2.00</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>average_montly_hours</th>\n      <td>2388.0</td>\n      <td>160.342546</td>\n      <td>38.849089</td>\n      <td>96.00</td>\n      <td>136.00</td>\n      <td>149.00</td>\n      <td>160.00</td>\n      <td>310.0</td>\n    </tr>\n    <tr>\n      <th>time_spend_company</th>\n      <td>2388.0</td>\n      <td>3.180486</td>\n      <td>0.946232</td>\n      <td>2.00</td>\n      <td>3.00</td>\n      <td>3.00</td>\n      <td>3.00</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>Work_accident</th>\n      <td>2388.0</td>\n      <td>0.092965</td>\n      <td>0.290444</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>left</th>\n      <td>2388.0</td>\n      <td>0.656198</td>\n      <td>0.475076</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>promotion_last_5years</th>\n      <td>2388.0</td>\n      <td>0.015494</td>\n      <td>0.123533</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 研究项目数为2的这部分员工\n",
    "data[data[\"number_project\"]==2].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:26:25.123021100Z",
     "start_time": "2024-05-28T02:26:25.054156300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                        count        mean        std     min     25%     50%  \\\nsatisfaction_level     1567.0    0.412948   0.054379    0.10    0.38    0.41   \nlast_evaluation        1567.0    0.518571   0.064562    0.45    0.48    0.51   \nnumber_project         1567.0    2.000000   0.000000    2.00    2.00    2.00   \naverage_montly_hours   1567.0  146.310147  19.435443  126.00  135.00  145.00   \ntime_spend_company     1567.0    3.027441   0.242144    2.00    3.00    3.00   \nWork_accident          1567.0    0.046586   0.210817    0.00    0.00    0.00   \nleft                   1567.0    1.000000   0.000000    1.00    1.00    1.00   \npromotion_last_5years  1567.0    0.007658   0.087202    0.00    0.00    0.00   \n\n                          75%     max  \nsatisfaction_level       0.44    0.89  \nlast_evaluation          0.54    1.00  \nnumber_project           2.00    2.00  \naverage_montly_hours   153.00  310.00  \ntime_spend_company       3.00    5.00  \nWork_accident            0.00    1.00  \nleft                     1.00    1.00  \npromotion_last_5years    0.00    1.00  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>satisfaction_level</th>\n      <td>1567.0</td>\n      <td>0.412948</td>\n      <td>0.054379</td>\n      <td>0.10</td>\n      <td>0.38</td>\n      <td>0.41</td>\n      <td>0.44</td>\n      <td>0.89</td>\n    </tr>\n    <tr>\n      <th>last_evaluation</th>\n      <td>1567.0</td>\n      <td>0.518571</td>\n      <td>0.064562</td>\n      <td>0.45</td>\n      <td>0.48</td>\n      <td>0.51</td>\n      <td>0.54</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>number_project</th>\n      <td>1567.0</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.00</td>\n      <td>2.00</td>\n      <td>2.00</td>\n      <td>2.00</td>\n      <td>2.00</td>\n    </tr>\n    <tr>\n      <th>average_montly_hours</th>\n      <td>1567.0</td>\n      <td>146.310147</td>\n      <td>19.435443</td>\n      <td>126.00</td>\n      <td>135.00</td>\n      <td>145.00</td>\n      <td>153.00</td>\n      <td>310.00</td>\n    </tr>\n    <tr>\n      <th>time_spend_company</th>\n      <td>1567.0</td>\n      <td>3.027441</td>\n      <td>0.242144</td>\n      <td>2.00</td>\n      <td>3.00</td>\n      <td>3.00</td>\n      <td>3.00</td>\n      <td>5.00</td>\n    </tr>\n    <tr>\n      <th>Work_accident</th>\n      <td>1567.0</td>\n      <td>0.046586</td>\n      <td>0.210817</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>left</th>\n      <td>1567.0</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>promotion_last_5years</th>\n      <td>1567.0</td>\n      <td>0.007658</td>\n      <td>0.087202</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 研究项目数为2且离职的员工\n",
    "p2_1 = data[(data[\"number_project\"]==2)&(data[\"left\"]==1)]\n",
    "p2_1.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:05.702021800Z",
     "start_time": "2024-05-28T02:30:05.642549600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                              1059\nsatisfaction_level             0.7\nlast_evaluation               0.93\nnumber_project                   2\naverage_montly_hours           310\ntime_spend_company               3\nWork_accident                    0\nleft                             1\npromotion_last_5years            0\nsales                  product_mng\nsalary                         low",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1059</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>satisfaction_level</th>\n      <td>0.7</td>\n    </tr>\n    <tr>\n      <th>last_evaluation</th>\n      <td>0.93</td>\n    </tr>\n    <tr>\n      <th>number_project</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>average_montly_hours</th>\n      <td>310</td>\n    </tr>\n    <tr>\n      <th>time_spend_company</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Work_accident</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>left</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>promotion_last_5years</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>sales</th>\n      <td>product_mng</td>\n    </tr>\n    <tr>\n      <th>salary</th>\n      <td>low</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2_1[p2_1[\"average_montly_hours\"]==310].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5 特征探索：平均每月工作时长与是否离职的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:07.762311200Z",
     "start_time": "2024-05-28T02:30:07.681986400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "count    14999.000000\nmean       201.050337\nstd         49.943099\nmin         96.000000\n25%        156.000000\n50%        200.000000\n75%        245.000000\nmax        310.000000\nName: average_montly_hours, dtype: float64"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"average_montly_hours\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:10.768910700Z",
     "start_time": "2024-05-28T02:30:10.617762700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x400 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,2,figsize=(8,4),dpi=100)\n",
    "\n",
    "sns.boxplot(y=data[\"average_montly_hours\"],width=0.2,color=\"c\",ax=ax[0])\n",
    "sns.boxplot(data=data,y=\"average_montly_hours\",x=\"left\",width=0.4,ax=ax[1])\n",
    "\n",
    "# 图形修饰\n",
    "ax[0].set_xlabel(\"总体员工\")\n",
    "ax[1].set_yticks([])\n",
    "ax[1].set_ylabel(\"\")\n",
    "ax[1].set_xlabel(\"\")\n",
    "ax[1].set_xticklabels([\"在职\",\"离职\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过箱型图可以看出，离职人员的平均工作时长相对比较长，最小值、中位数、最大值都明显比在职员工高出一大截。所以有理由推测，加班是导致员工离职的一个原因。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:13.942650200Z",
     "start_time": "2024-05-28T02:30:13.886616100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "     average_montly_hours                                \n                      min        max      mean     median\nleft                                                     \n0                4.363636  13.045455  9.048191   9.000000\n1                5.727273  14.090909  9.428146  10.181818",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"4\" halign=\"left\">average_montly_hours</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>min</th>\n      <th>max</th>\n      <th>mean</th>\n      <th>median</th>\n    </tr>\n    <tr>\n      <th>left</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4.363636</td>\n      <td>13.045455</td>\n      <td>9.048191</td>\n      <td>9.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5.727273</td>\n      <td>14.090909</td>\n      <td>9.428146</td>\n      <td>10.181818</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"left\").agg({\"average_montly_hours\":[\"min\",\"max\",\"mean\",\"median\"]})/22"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每月按照22个工作日计算，可以看出，离职人员中一半以上每天工作时长超过10小时，最长的平均每个工作日工作超过14小时，再一次证明严重的加班是导致员工离职的重要因素。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.6 特征探索：工作年限和是否离职的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:15.701020400Z",
     "start_time": "2024-05-28T02:30:15.656989300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "3     6443\n2     3244\n4     2557\n5     1473\n6      718\n10     214\n7      188\n8      162\nName: time_spend_company, dtype: int64"
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"time_spend_company\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:17.556817400Z",
     "start_time": "2024-05-28T02:30:17.527886600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "2     3244\n3     6443\n4     2557\n5     1473\n6      718\n7      188\n8      162\n10     214\nName: time_spend_company, dtype: int64"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v3 = data[\"time_spend_company\"].value_counts()\n",
    "v3.sort_index(inplace=True)\n",
    "v3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:19.956663100Z",
     "start_time": "2024-05-28T02:30:19.938463Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "left                       0         1\ntime_spend_company                    \n2                   0.983662  0.016338\n3                   0.753841  0.246159\n4                   0.651936  0.348064\n5                   0.434487  0.565513\n6                   0.708914  0.291086\n7                   1.000000  0.000000\n8                   1.000000  0.000000\n10                  1.000000  0.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>left</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>time_spend_company</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>0.983662</td>\n      <td>0.016338</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.753841</td>\n      <td>0.246159</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.651936</td>\n      <td>0.348064</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.434487</td>\n      <td>0.565513</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.708914</td>\n      <td>0.291086</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v3_rate = data.groupby([\"time_spend_company\",\"left\"],as_index=False).size()\n",
    "v3_rate = v3_rate.pivot(index=\"time_spend_company\",columns=\"left\",values=\"size\")\n",
    "v3_rate = v3_rate.div(v3_rate.sum(axis=1), axis=0)\n",
    "v3_rate.fillna(0,inplace=True)\n",
    "v3_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:22.097834700Z",
     "start_time": "2024-05-28T02:30:21.734849Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x400 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4),dpi=100)\n",
    "\n",
    "# 绘制不同工作年限人员离职数量\n",
    "plt.subplot(121)\n",
    "sns.histplot(data=data,x=\"time_spend_company\",hue=\"left\"\n",
    "             ,multiple='stack'\n",
    "             ,shrink=0.8\n",
    "             ,discrete=True\n",
    "             ,alpha=0.7)\n",
    "\n",
    "# 绘制工作年限与是否离职的关系\n",
    "plt.subplot(122)\n",
    "plt.bar(x=v3_rate.index,height=v3_rate[1],color=\"#ff7f0e\",alpha=0.8)\n",
    "plt.bar(x=v3_rate.index,height=v3_rate[0],bottom=v3_rate[1],color=\"#1f77b4\",alpha=0.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看到：\n",
    "- 企业中3年人数最多，其次是2年，说明该公司主要是以年轻人为主；\n",
    "- 在各工作年限中，离职人员较集中于3,4,5,6年，而6年以上则相对比较稳定，基本上无人离职。这个其实也跟现在的市场状况基本吻合：对一个职场人员来说，3-5年是一个跳槽高峰期。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.7 特征探索：是否发生工作事故与是否离职的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:25.756839300Z",
     "start_time": "2024-05-28T02:30:25.702023700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0    12830\n1     2169\nName: Work_accident, dtype: int64"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Work_accident\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:27.341236400Z",
     "start_time": "2024-05-28T02:30:27.322293500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "left                  0         1\nWork_accident                    \n0              0.734840  0.265160\n1              0.922084  0.077916",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>left</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>Work_accident</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.734840</td>\n      <td>0.265160</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.922084</td>\n      <td>0.077916</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v4_rate = data.groupby([\"Work_accident\",\"left\"],as_index=False).size()\n",
    "v4_rate = v4_rate.pivot(index=\"Work_accident\",columns=\"left\",values=\"size\")\n",
    "v4_rate = v4_rate.div(v4_rate.sum(axis=1), axis=0)\n",
    "v4_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:30.042339Z",
     "start_time": "2024-05-28T02:30:29.794652400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x400 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,2,figsize=(12,4),dpi=100)\n",
    "\n",
    "sns.histplot(data=data,x=\"Work_accident\",hue=\"left\"\n",
    "             ,multiple='stack'\n",
    "             ,shrink=0.8\n",
    "             ,discrete=True\n",
    "             ,alpha=0.7\n",
    "             ,ax=ax[0])\n",
    "\n",
    "plt.bar(x=v4_rate.index,height=v4_rate[1],color=\"#ff7f0e\",alpha=0.8)\n",
    "plt.bar(x=v4_rate.index,height=v4_rate[0],bottom=v4_rate[1],color=\"#1f77b4\",alpha=0.8)\n",
    "\n",
    "# 图形修饰\n",
    "ax[0].set_xticks([0,1])\n",
    "ax[1].set_xticks([0,1])\n",
    "ax[1].set_xlabel(\"Work_accident\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，只有少部分员工出现过工作事故，并且其中只有少部分人选择离职，**说明是否出现工作事故与离职没有必然联系**。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.8 特征探索：五年内是否晋升与是否离职的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:33.195345300Z",
     "start_time": "2024-05-28T02:30:33.156072600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0    0.978732\n1    0.021268\nName: promotion_last_5years, dtype: float64"
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"promotion_last_5years\"].value_counts()/data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:34.838472Z",
     "start_time": "2024-05-28T02:30:34.624074700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=data,x=\"promotion_last_5years\",hue=\"left\"\n",
    "             ,multiple='stack'\n",
    "             ,shrink=0.4\n",
    "             ,discrete=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:37.288032900Z",
     "start_time": "2024-05-28T02:30:37.233674600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.24196185286103541"
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算5年内没有晋升的员工中，离职的占比\n",
    "data.loc[data[\"promotion_last_5years\"]==0,\"left\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:39.947650900Z",
     "start_time": "2024-05-28T02:30:39.888484200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.05956112852664577"
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算5年内获得晋升的员工中，离职的占比\n",
    "data.loc[data[\"promotion_last_5years\"]==1,\"left\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述图表及计算结果可以看出，该公司绝大部分员工（98%）在5年内都没有晋升，这部分员工的离职率为24%，将近四分之一。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.9 特征探索：岗位与是否离职的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:42.156049200Z",
     "start_time": "2024-05-28T02:30:42.096898200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "sales          4140\ntechnical      2720\nsupport        2229\nIT             1227\nproduct_mng     902\nmarketing       858\nRandD           787\naccounting      767\nhr              739\nmanagement      630\nName: sales, dtype: int64"
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v5 = data[\"sales\"].value_counts()\n",
    "v5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:45.055339Z",
     "start_time": "2024-05-28T02:30:45.007635Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "sales          1014\ntechnical       697\nsupport         555\nIT              273\nproduct_mng     198\nmarketing       203\nRandD           121\naccounting      204\nhr              215\nmanagement       91\nName: sales, dtype: int64"
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v5_1 = data.loc[data[\"left\"]==1,\"sales\"].value_counts()\n",
    "v5_1 = v5_1.reindex(index=v5.index) #将索引与v5保持一致\n",
    "v5_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:47.546314Z",
     "start_time": "2024-05-28T02:30:47.496274700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "left                0         1\nsales                          \nsales        0.755072  0.244928\ntechnical    0.743750  0.256250\nsupport      0.751009  0.248991\nIT           0.777506  0.222494\nproduct_mng  0.780488  0.219512\nmarketing    0.763403  0.236597\nRandD        0.846252  0.153748\naccounting   0.734029  0.265971\nhr           0.709066  0.290934\nmanagement   0.855556  0.144444",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>left</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>sales</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>sales</th>\n      <td>0.755072</td>\n      <td>0.244928</td>\n    </tr>\n    <tr>\n      <th>technical</th>\n      <td>0.743750</td>\n      <td>0.256250</td>\n    </tr>\n    <tr>\n      <th>support</th>\n      <td>0.751009</td>\n      <td>0.248991</td>\n    </tr>\n    <tr>\n      <th>IT</th>\n      <td>0.777506</td>\n      <td>0.222494</td>\n    </tr>\n    <tr>\n      <th>product_mng</th>\n      <td>0.780488</td>\n      <td>0.219512</td>\n    </tr>\n    <tr>\n      <th>marketing</th>\n      <td>0.763403</td>\n      <td>0.236597</td>\n    </tr>\n    <tr>\n      <th>RandD</th>\n      <td>0.846252</td>\n      <td>0.153748</td>\n    </tr>\n    <tr>\n      <th>accounting</th>\n      <td>0.734029</td>\n      <td>0.265971</td>\n    </tr>\n    <tr>\n      <th>hr</th>\n      <td>0.709066</td>\n      <td>0.290934</td>\n    </tr>\n    <tr>\n      <th>management</th>\n      <td>0.855556</td>\n      <td>0.144444</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v5_rate = data.groupby([\"sales\",\"left\"],as_index=False).size()\n",
    "v5_rate = v5_rate.pivot(index=\"sales\",columns=\"left\",values=\"size\")\n",
    "v5_rate[\"total\"] = v5_rate[0]+v5_rate[1]\n",
    "v5_rate = v5_rate.sort_values(by=\"total\",ascending=False)\n",
    "v5_rate = v5_rate.drop(columns=\"total\")\n",
    "v5_rate = v5_rate.div(v5_rate.sum(axis=1), axis=0)\n",
    "# v5_rate.fillna(0,inplace=True)\n",
    "v5_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:50.476887Z",
     "start_time": "2024-05-28T02:30:49.997395700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x1000 with 3 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = plt.get_cmap('Set3')(np.linspace(0.2, 0.7, len(v2)))\n",
    "plt.figure(figsize=(12,10),dpi=100)\n",
    "grid = plt.GridSpec(2, 2, wspace=0.2, hspace=0.3)\n",
    "\n",
    "# 绘制不同岗位人员离职数量\n",
    "plt.subplot(grid[0, :])\n",
    "plt.barh(y=v5.index,width=v5,alpha=0.7)\n",
    "plt.barh(y=v5_1.index,width=v5_1,color=\"#ff7f0e\",alpha=0.7)\n",
    "\n",
    "# 绘制不同岗位离职人员占比\n",
    "plt.subplot(grid[1, 0])\n",
    "plt.barh(y=v5_rate.index,width=v5_rate[1],color=\"#ff7f0e\",alpha=0.7)\n",
    "plt.barh(y=v5_rate.index,width=v5_rate[0],left=v5_rate[1],color=\"#1f77b4\",alpha=0.7)\n",
    "\n",
    "# 绘制不同岗位所占百分比环形图\n",
    "plt.subplot(grid[1, 1])\n",
    "plt.pie(v5,autopct=\"%.2f%%\",radius=1,pctdistance=0.8,labels=v5.index,colors=colors,wedgeprops={\"width\":0.4});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过图形可以看出，该公司销售岗位占比最多，达到27.6%，但是各岗位的离职率差不多，基本上都在20%左右，其中hr岗位离职率稍微高一些，达到29%。所以，不同岗位与离职关系并不是很大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.10 特征探索：薪资水平与是否离职的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:53.495599500Z",
     "start_time": "2024-05-28T02:30:53.446614300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "low       7316\nmedium    6446\nhigh      1237\nName: salary, dtype: int64"
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"salary\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:55.754205400Z",
     "start_time": "2024-05-28T02:30:55.705597200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "left           0         1\nsalary                    \nlow     0.703116  0.296884\nmedium  0.795687  0.204313\nhigh    0.933711  0.066289",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>left</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>salary</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>low</th>\n      <td>0.703116</td>\n      <td>0.296884</td>\n    </tr>\n    <tr>\n      <th>medium</th>\n      <td>0.795687</td>\n      <td>0.204313</td>\n    </tr>\n    <tr>\n      <th>high</th>\n      <td>0.933711</td>\n      <td>0.066289</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v6_rate = data.groupby([\"salary\",\"left\"],as_index=False).size()\n",
    "v6_rate = v6_rate.pivot(index=\"salary\",columns=\"left\",values=\"size\")\n",
    "v6_rate = v6_rate.div(v6_rate.sum(axis=1), axis=0)\n",
    "v6_rate = v6_rate.reindex(['low', 'medium', 'high'])\n",
    "v6_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:30:57.835320300Z",
     "start_time": "2024-05-28T02:30:57.561885700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x400 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,2,figsize=(12,4),dpi=100)\n",
    "\n",
    "sns.histplot(data=data,x=\"salary\",hue=\"left\"\n",
    "             ,multiple='stack'\n",
    "             ,shrink=0.8\n",
    "             ,discrete=True\n",
    "             ,alpha=0.7\n",
    "             ,ax=ax[0])\n",
    "\n",
    "plt.bar(x=v6_rate.index,height=v6_rate[1],color=\"#ff7f0e\",alpha=0.8)\n",
    "plt.bar(x=v6_rate.index,height=v6_rate[0],bottom=v6_rate[1],color=\"#1f77b4\",alpha=0.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，薪资水平分为三个水平：低等、中等和高等，低等水平人数最多，离职率也最高，接近30%。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "综上所述，员工是否离职的重要因素主要是就是参加的项目数、平均每月工作时长、工作年限、晋升机会及薪资水平，这些所有的因素其实就是工作量、工作时长和升职加薪。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 特征工程&建立模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1 编码：将文本型变量变为数值型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:31:40.764183200Z",
     "start_time": "2024-05-28T02:31:40.098650900Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder   # 根据类别特征的顺序编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:31:47.287346100Z",
     "start_time": "2024-05-28T02:31:47.206679Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n0                0.38             0.53               2                   157   \n1                0.80             0.86               5                   262   \n2                0.11             0.88               7                   272   \n3                0.72             0.87               5                   223   \n4                0.37             0.52               2                   159   \n\n   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n0                   3              0     1                      0    7.0   \n1                   6              0     1                      0    7.0   \n2                   4              0     1                      0    7.0   \n3                   5              0     1                      0    7.0   \n4                   3              0     1                      0    7.0   \n\n   salary  \n0     1.0  \n1     2.0  \n2     2.0  \n3     1.0  \n4     1.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>satisfaction_level</th>\n      <th>last_evaluation</th>\n      <th>number_project</th>\n      <th>average_montly_hours</th>\n      <th>time_spend_company</th>\n      <th>Work_accident</th>\n      <th>left</th>\n      <th>promotion_last_5years</th>\n      <th>sales</th>\n      <th>salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.38</td>\n      <td>0.53</td>\n      <td>2</td>\n      <td>157</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.80</td>\n      <td>0.86</td>\n      <td>5</td>\n      <td>262</td>\n      <td>6</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.11</td>\n      <td>0.88</td>\n      <td>7</td>\n      <td>272</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.72</td>\n      <td>0.87</td>\n      <td>5</td>\n      <td>223</td>\n      <td>5</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.37</td>\n      <td>0.52</td>\n      <td>2</td>\n      <td>159</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = OrdinalEncoder().fit_transform(data.iloc[:,-2:])\n",
    "d1 = pd.DataFrame(d1,columns=data.iloc[:,-2:].columns)\n",
    "df = pd.concat([data.iloc[:,:-2],d1],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2 提取特征和标签并切分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:31:59.552811300Z",
     "start_time": "2024-05-28T02:31:59.536855400Z"
    }
   },
   "outputs": [],
   "source": [
    "X = df.loc[:,df.columns!=\"left\"]\n",
    "y = df.loc[:,\"left\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:32:02.001509500Z",
     "start_time": "2024-05-28T02:32:01.459181500Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,y,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:32:03.893792Z",
     "start_time": "2024-05-28T02:32:03.849108700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(10499, 9)"
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:32:05.137872200Z",
     "start_time": "2024-05-28T02:32:05.081869600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(4500, 9)"
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtest.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3 初步建模：建立benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:32:16.532035Z",
     "start_time": "2024-05-28T02:32:16.252087600Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression as LR\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.metrics import roc_auc_score,roc_curve,auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:32:18.825940700Z",
     "start_time": "2024-05-28T02:32:18.600229Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集上的预测准确率为： 0.7646442518335079\n",
      "测试集上的预测准确率为： 0.7666666666666667\n"
     ]
    }
   ],
   "source": [
    "clf = LR(max_iter=1000)\n",
    "clf = clf.fit(Xtrain,Ytrain)\n",
    "print(\"训练集上的预测准确率为：\",clf.score(Xtrain,Ytrain))\n",
    "print(\"测试集上的预测准确率为：\",clf.score(Xtest,Ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:32:30.635574400Z",
     "start_time": "2024-05-28T02:32:30.577290200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([[7387,  579],\n       [1892,  641]], dtype=int64)"
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(Ytrain,clf.predict(Xtrain))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:32:33.524134800Z",
     "start_time": "2024-05-28T02:32:33.480270200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.5901878532519624"
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(Ytrain,clf.predict(Xtrain))   # roc——auc面积,代表模型预测能力,最好的为1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:33:05.700644900Z",
     "start_time": "2024-05-28T02:33:05.629780Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.93      0.86      7966\n",
      "           1       0.53      0.25      0.34      2533\n",
      "\n",
      "    accuracy                           0.76     10499\n",
      "   macro avg       0.66      0.59      0.60     10499\n",
      "weighted avg       0.73      0.76      0.73     10499\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Ytrain,clf.predict(Xtrain)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.4 测试数据归一化对模型结果的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:33:10.220611500Z",
     "start_time": "2024-05-28T02:33:10.195175900Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:33:11.716834400Z",
     "start_time": "2024-05-28T02:33:11.643654100Z"
    }
   },
   "outputs": [],
   "source": [
    "mms = MinMaxScaler()\n",
    "mms = mms.fit(Xtrain)\n",
    "Xtrain_mms = mms.transform(Xtrain)\n",
    "Xtest_mms = mms.transform(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:33:13.296911Z",
     "start_time": "2024-05-28T02:33:13.193009700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集上的预测准确率为： 0.7655014763310791\n",
      "测试集上的预测准确率为： 0.7666666666666667\n"
     ]
    }
   ],
   "source": [
    "# 重新建模\n",
    "clf = LR()\n",
    "clf = clf.fit(Xtrain_mms,Ytrain)\n",
    "print(\"训练集上的预测准确率为：\",clf.score(Xtrain_mms,Ytrain))\n",
    "print(\"测试集上的预测准确率为：\",clf.score(Xtest_mms,Ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:33:15.776534500Z",
     "start_time": "2024-05-28T02:33:15.688407500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([[3190,  272],\n       [ 778,  260]], dtype=int64)"
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(Ytest,clf.predict(Xtest_mms))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:33:17.587229600Z",
     "start_time": "2024-05-28T02:33:17.559322700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.585957196715454"
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(Ytest,clf.predict(Xtest_mms))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:33:19.983211500Z",
     "start_time": "2024-05-28T02:33:19.930411400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.92      0.86      3462\n",
      "           1       0.49      0.25      0.33      1038\n",
      "\n",
      "    accuracy                           0.77      4500\n",
      "   macro avg       0.65      0.59      0.59      4500\n",
      "weighted avg       0.73      0.77      0.74      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Ytest,clf.predict(Xtest_mms)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，对于这个数据集来说，数据归一化对模型效果提升作用非常有限，模型AUC面积仅仅提升一点。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 模型调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:33:22.870323300Z",
     "start_time": "2024-05-28T02:33:22.840659600Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:35:23.717418900Z",
     "start_time": "2024-05-28T02:35:23.650720400Z"
    }
   },
   "outputs": [],
   "source": [
    "parameters = {'C':np.linspace(0.01,10,10)\n",
    "              ,'solver':['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n",
    "              ,'class_weight':[{1:i} for i in np.linspace(1,10,10)]}\n",
    "# C:正则化系数，越大越不容易过拟合\n",
    "# solvers: 优化算法,有newton-cg, lbfgs, liblinear, sag, saga\n",
    "# class_weight: 类别权重，越大越容易预测该类别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:35:25.479096500Z",
     "start_time": "2024-05-28T02:35:25.453533Z"
    }
   },
   "outputs": [],
   "source": [
    "GS = GridSearchCV(LR(max_iter=1000), parameters, cv=5,scoring=\"f1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "ExecuteTime": {
     "end_time": "2024-05-28T02:53:19.645487400Z",
     "start_time": "2024-05-28T02:35:28.545584900Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "GridSearchCV(cv=5, estimator=LogisticRegression(max_iter=1000),\n             param_grid={'C': array([ 0.01,  1.12,  2.23,  3.34,  4.45,  5.56,  6.67,  7.78,  8.89,\n       10.  ]),\n                         'class_weight': [{1: 1.0}, {1: 2.0}, {1: 3.0},\n                                          {1: 4.0}, {1: 5.0}, {1: 6.0},\n                                          {1: 7.0}, {1: 8.0}, {1: 9.0},\n                                          {1: 10.0}],\n                         'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag',\n                                    'saga']},\n             scoring='f1')",
      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(max_iter=1000),\n             param_grid={&#x27;C&#x27;: array([ 0.01,  1.12,  2.23,  3.34,  4.45,  5.56,  6.67,  7.78,  8.89,\n       10.  ]),\n                         &#x27;class_weight&#x27;: [{1: 1.0}, {1: 2.0}, {1: 3.0},\n                                          {1: 4.0}, {1: 5.0}, {1: 6.0},\n                                          {1: 7.0}, {1: 8.0}, {1: 9.0},\n                                          {1: 10.0}],\n                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n                                    &#x27;saga&#x27;]},\n             scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(max_iter=1000),\n             param_grid={&#x27;C&#x27;: array([ 0.01,  1.12,  2.23,  3.34,  4.45,  5.56,  6.67,  7.78,  8.89,\n       10.  ]),\n                         &#x27;class_weight&#x27;: [{1: 1.0}, {1: 2.0}, {1: 3.0},\n                                          {1: 4.0}, {1: 5.0}, {1: 6.0},\n                                          {1: 7.0}, {1: 8.0}, {1: 9.0},\n                                          {1: 10.0}],\n                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n                                    &#x27;saga&#x27;]},\n             scoring=&#x27;f1&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ============【timewarning:20min】==============\n",
    "GS.fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:53:35.051332200Z",
     "start_time": "2024-05-28T02:53:35.021680600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "{'C': 0.01, 'class_weight': {1: 3.0}, 'solver': 'saga'}"
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:53:37.122974100Z",
     "start_time": "2024-05-28T02:53:37.086351600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "LogisticRegression(C=0.01, class_weight={1: 3.0}, max_iter=1000, solver='saga')",
      "text/html": "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.01, class_weight={1: 3.0}, max_iter=1000, solver=&#x27;saga&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.01, class_weight={1: 3.0}, max_iter=1000, solver=&#x27;saga&#x27;)</pre></div></div></div></div></div>"
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_LR = GS.best_estimator_\n",
    "best_LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:53:41.280296Z",
     "start_time": "2024-05-28T02:53:40.152167Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集上的预测准确率为： 0.7687398799885703\n",
      "测试集上的预测准确率为： 0.7675555555555555\n"
     ]
    }
   ],
   "source": [
    "best_LR.fit(Xtrain,Ytrain)\n",
    "print(\"训练集上的预测准确率为：\",best_LR.score(Xtrain,Ytrain))\n",
    "print(\"测试集上的预测准确率为：\",best_LR.score(Xtest,Ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:53:43.738583400Z",
     "start_time": "2024-05-28T02:53:43.669049700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([[2597,  865],\n       [ 181,  857]], dtype=int64)"
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(Ytest,best_LR.predict(Xtest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:53:45.897811300Z",
     "start_time": "2024-05-28T02:53:45.864342500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.7878853147133369"
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(Ytest,best_LR.predict(Xtest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:53:48.091378900Z",
     "start_time": "2024-05-28T02:53:47.975999600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.75      0.83      3462\n",
      "           1       0.50      0.83      0.62      1038\n",
      "\n",
      "    accuracy                           0.77      4500\n",
      "   macro avg       0.72      0.79      0.73      4500\n",
      "weighted avg       0.83      0.77      0.78      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Ytest,best_LR.predict(Xtest)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4 color=\"bule\">**也可以试一下HalvingGridSearchCV，它会比常规网格搜索要快一些**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:55:32.685498900Z",
     "start_time": "2024-05-28T02:55:32.639074700Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.experimental import enable_halving_search_cv\n",
    "from sklearn.model_selection import HalvingGridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:55:33.963499600Z",
     "start_time": "2024-05-28T02:55:33.943055300Z"
    }
   },
   "outputs": [],
   "source": [
    "parameters = {'C':np.linspace(0.01,10,10)\n",
    "              ,'solver':['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n",
    "              ,'class_weight':[{1:i} for i in np.linspace(1,10,10)]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:55:36.011074900Z",
     "start_time": "2024-05-28T02:55:35.976687800Z"
    }
   },
   "outputs": [],
   "source": [
    "HGS = HalvingGridSearchCV(LR(max_iter=1000), parameters, cv=5,scoring=\"f1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "ExecuteTime": {
     "end_time": "2024-05-28T02:56:23.116392400Z",
     "start_time": "2024-05-28T02:55:37.215786500Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1609: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, \"true nor predicted\", \"F-score is\", len(true_sum))\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "HalvingGridSearchCV(estimator=LogisticRegression(max_iter=1000),\n                    param_grid={'C': array([ 0.01,  1.12,  2.23,  3.34,  4.45,  5.56,  6.67,  7.78,  8.89,\n       10.  ]),\n                                'class_weight': [{1: 1.0}, {1: 2.0}, {1: 3.0},\n                                                 {1: 4.0}, {1: 5.0}, {1: 6.0},\n                                                 {1: 7.0}, {1: 8.0}, {1: 9.0},\n                                                 {1: 10.0}],\n                                'solver': ['newton-cg', 'lbfgs', 'liblinear',\n                                           'sag', 'saga']},\n                    scoring='f1')",
      "text/html": "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>HalvingGridSearchCV(estimator=LogisticRegression(max_iter=1000),\n                    param_grid={&#x27;C&#x27;: array([ 0.01,  1.12,  2.23,  3.34,  4.45,  5.56,  6.67,  7.78,  8.89,\n       10.  ]),\n                                &#x27;class_weight&#x27;: [{1: 1.0}, {1: 2.0}, {1: 3.0},\n                                                 {1: 4.0}, {1: 5.0}, {1: 6.0},\n                                                 {1: 7.0}, {1: 8.0}, {1: 9.0},\n                                                 {1: 10.0}],\n                                &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;,\n                                           &#x27;sag&#x27;, &#x27;saga&#x27;]},\n                    scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">HalvingGridSearchCV</label><div class=\"sk-toggleable__content\"><pre>HalvingGridSearchCV(estimator=LogisticRegression(max_iter=1000),\n                    param_grid={&#x27;C&#x27;: array([ 0.01,  1.12,  2.23,  3.34,  4.45,  5.56,  6.67,  7.78,  8.89,\n       10.  ]),\n                                &#x27;class_weight&#x27;: [{1: 1.0}, {1: 2.0}, {1: 3.0},\n                                                 {1: 4.0}, {1: 5.0}, {1: 6.0},\n                                                 {1: 7.0}, {1: 8.0}, {1: 9.0},\n                                                 {1: 10.0}],\n                                &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;,\n                                           &#x27;sag&#x27;, &#x27;saga&#x27;]},\n                    scoring=&#x27;f1&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ============【timewarning:2min】==============\n",
    "HGS.fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:56:58.769122Z",
     "start_time": "2024-05-28T02:56:58.715000800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "{'C': 4.45, 'class_weight': {1: 4.0}, 'solver': 'liblinear'}"
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HGS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:57:01.439437600Z",
     "start_time": "2024-05-28T02:57:01.404564700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "LogisticRegression(C=4.45, class_weight={1: 4.0}, max_iter=1000,\n                   solver='liblinear')",
      "text/html": "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=4.45, class_weight={1: 4.0}, max_iter=1000,\n                   solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=4.45, class_weight={1: 4.0}, max_iter=1000,\n                   solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_LR1 = HGS.best_estimator_\n",
    "best_LR1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:57:04.379530Z",
     "start_time": "2024-05-28T02:57:04.270633600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集上的预测准确率为： 0.731307743594628\n",
      "测试集上的预测准确率为： 0.7266666666666667\n"
     ]
    }
   ],
   "source": [
    "best_LR1.fit(Xtrain,Ytrain)\n",
    "print(\"训练集上的预测准确率为：\",best_LR1.score(Xtrain,Ytrain))\n",
    "print(\"测试集上的预测准确率为：\",best_LR1.score(Xtest,Ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:57:06.456165600Z",
     "start_time": "2024-05-28T02:57:06.364493300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([[2377, 1085],\n       [ 145,  893]], dtype=int64)"
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(Ytest,best_LR1.predict(Xtest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:57:08.926531400Z",
     "start_time": "2024-05-28T02:57:08.863992Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.7734528138701608"
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(Ytest,best_LR1.predict(Xtest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:57:10.845681700Z",
     "start_time": "2024-05-28T02:57:10.737117600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.69      0.79      3462\n",
      "           1       0.45      0.86      0.59      1038\n",
      "\n",
      "    accuracy                           0.73      4500\n",
      "   macro avg       0.70      0.77      0.69      4500\n",
      "weighted avg       0.83      0.73      0.75      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Ytest,best_LR1.predict(Xtest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:57:14.672004500Z",
     "start_time": "2024-05-28T02:57:14.572519Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([[-2.58277027,  0.05688853, -0.25272109,  0.00465241,  0.37460666,\n        -0.99533914, -0.41573015,  0.03998002,  0.01823162]])"
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看各特征的回归系数\n",
    "best_LR.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T02:57:17.755551600Z",
     "start_time": "2024-05-28T02:57:17.719837600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[('satisfaction_level', -2.5827702730081015),\n ('Work_accident', -0.9953391423707594),\n ('promotion_last_5years', -0.4157301489748747),\n ('time_spend_company', 0.37460666387570585),\n ('number_project', -0.2527210948869483),\n ('last_evaluation', 0.05688852718930408),\n ('sales', 0.03998001648870062),\n ('salary', 0.018231622142073363),\n ('average_montly_hours', 0.004652411450501229)]"
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted([*zip(Xtrain.columns,best_LR.coef_.ravel())],key=lambda x: abs(x[1]),reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
